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DTSTAMP:20260522T150119Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181114T110000
DTEND;TZID=America/Chicago:20181114T113000
UID:submissions.supercomputing.org_SC18_sess205_pap194@linklings.com
SUMMARY:DRAGON: Breaking GPU Memory Capacity Limits with Direct NVM Access
DESCRIPTION:Pak Markthub (Tokyo Institute of Technology); Mehmet E. Belvir
 anli, Seyong Lee, and Jeffrey S. Vetter (Oak Ridge National Laboratory); a
 nd Satoshi Matsuoka (RIKEN, Tokyo Institute of Technology)\n\nHeterogeneou
 s computing with accelerators is growing in importance in high performance
  computing (HPC). Recently, application datasets have expanded beyond the 
 memory capacity of these accelerators, and often beyond the capacity of th
 eir hosts. Meanwhile, nonvolatile memory (NVM) storage has emerged as a pe
 rvasive component in HPC systems because NVM provides massive amounts of m
 emory capacity at affordable cost. Currently, for accelerator applications
  to use NVM, they must manually orchestrate data movement across multiple 
 memories and this approach only performs well for applications with simple
  access behaviors. To address this issue, we developed DRAGON, a solution 
 that enables all classes of GP-GPU applications to transparently compute o
 n terabyte datasets residing in NVM. DRAGON leverages the page-faulting me
 chanism on the recent NVIDIA GPUs by extending capabilities of CUDA Unifie
 d Memory (UM). Our experimental results show that DRAGON transparently exp
 ands memory capacity and obtain additional speedups via automated I/O and 
 data transfer overlapping.\n\nTag: GPUs, Memory, NVRAM, Performance, Syste
 m Software, Tools\n\nRegistration Category: Tech Program Reg Pass\n\nSessi
 on Chair: Rosa M. Badia (Barcelona Supercomputing Center (BSC); Polytechni
 c University of Catalonia, Spain)\n\n
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